Process control systems for industrial applications have existed in many forms for a number of years. Several products, methods and systems have been developed to monitor and validate sensors in such systems and diagnose faults which might occur in such systems. Standard fault management products are based on creating predictive models using multivariate statistics and/or abnormal operation information. Such models, including neural networks and other complex models, are often difficult for an end-user (such as a manufacturing engineer or other operator) to understand, maintain or even trust. In addition, such models may not be scalable to real fault conditions and are only as good as the set of data provided, which by nature does not encompass all process operating conditions. Therefore, such models may not be robust enough to reliably predict or identify all abnormal or non-steady-state conditions, i.e., the reason for a fault management tool in the first place. Also, existing diagnostic methods and systems based on boolean logic may suffer from “diagnostic instability.” In addition, the cost of prior art systems such as statistical models, expert systems, neural networks, smart sensors and redundant sensors may be very high.
Several methods and software systems have been developed to monitor process control systems and perform sensor validation. For example, U.S. Pat. No. 6,246,972 (Klimasauskas) discloses an analyzer for modeling and optimizing maintenance operation. U.S. Pat. No. 6,356,857 (Qin et al.) discloses a sensor validation apparatus and method, but not a fault analyzer. Other patents, such as U.S. Pat. No. 5,987,398 (Halverson et al.), disclose the use of statistical process control in the context of a process control system. Other examples of sensor validation, fault analyzer or faulty sensor identification patents include U.S. Pat. No. 6,594,620 (Qin et al.), U.S. Pat. No. 5,442,562 (Hopkins et al.) and U.S. Pat. No. 5,949,678 (Wold et al.). None of the foregoing patents utilizes the method of the present invention, which has significant advantages, as described below. Furthermore, no prior art system provides for simultaneous real-time sensor data collection, sensor validation and predictive fault analysis, and statistical process control.
There is a need, therefore, for an improved method and system of monitoring, validation and predictive fault analysis for process control systems, such as those in chemical plants, which overcomes these deficiencies; includes an evaluation of process models derived from normal data and using real-time measured process data; and provides, among other things, continuous and direct analysis which alerts end-users to potential underlying process problems.